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Dear Colleagues,

Following people’s awareness of the importance of the reliabilty, safety and maintainability of industrial systems for a long period of time, innovative technologies for health monitoring and diagnostics of industrial systems have attracted increasing attention. In particular, with the rapid advances of artificial intelligence, intelligent Internet of Things (IoT) and industrial big data technologies, there have been increasing interests in the development of advanced artificial intelligence algorithms in order to address the challenges in the fields of condition monitoring, anomaly detection, fault prognostics and diagnostics of various industrial systems. Recently, diverse kinds of artificial intelligence algorithms, such as convolution neural network, adversarial adaptation network and extreme learning machine, have been developed for health monitoring and diagnostics in the light of massive monitoring data collected by sensors and IoT devices.

The aim of this Special Issue is to provide a platform for scientists, engineers and industrial practitioners to present their latest theoretical and technological advancements in artificial intelligence-enhanced health monitoring and diagnositics for industrial systems. High-quality research articles, short communication and reviews are welcome. Research studies that seek to address recent developments in advanced artificial intelligence algorithms are of special interest, such as deep learning, ensemble learning, transfer learning and reinforcement learning, and are well suited for enhancing the health monitoring, diagnositics and prognostics of industrial systems.

Papers are solicited in but are not limited to the following and related topics:

Artificial intelligence algorithms for health monitoring and fault diagnosis;
Big data mining methods for anomaly detection;
Deep learning methods for intelligent fault prognostics;
Deep transfer learning algorithms for fault diagnosis with small fault samples;
Deep reinforcement learning methods for prognostics and health management;
Artificial intelligence-based edge and cloud computing for health monitoring.

Prof. Dr. Jun Wu
Dr. Zhaojun Steven Li
Prof. Dr. Yi Qin
Dr. Carman K.M. Lee
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Related Special Issue

Published Papers (1 paper)

Research

15 pages, 3295 KiB

Open AccessArticle

Track Irregularity Identification Method of High-Speed Railway Based on CNN-Bi-LSTM

by Jinsong Yang, Jinzhao Liu, Jianfeng Guo and Kai Tao

Sensors 2024, 24(9), 2861; https://doi.org/10.3390/s24092861 - 30 Apr 2024

Viewed by 417

Abstract

Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train. Full article

(This article belongs to the Special Issue Artificial Intelligence Enhanced Health Monitoring and Diagnostics: 2nd Edition)

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